FOMO: Fairness-Oriented Multi-objective Optimization

Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of error versus fairness. Our lab takes a different approach, developing flexible optimizers that characterize the error-fairness tradeoff surface by integrating multi-objective optimization into existing machine learning models.

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